Research methods

• Experimental method

  Involves the manipulation of an independent variable (IV) to measure the effect on the dependent variable (DV). Experiments may be laboratory, field, natural or quasi.
• Aim

  A general statement of what the researcher intends to investigate, the purpose of the study.
• Hypothesis

  A clear, precise, testable statement that states the relationship between the variables to be investigated. Stated at the outset of any study.
• Directional hypothesis 


  States the direction of the difference or relationship.
• Non- directional hypothesis 


  Does not state the direction of the difference or relationship.
• Variables

  Any 'thing' that can vary or change within an investigation. Variables are generally used in experiments to determine if changes in one thing result in changes to another.
• Independent variable (IV)

  Some aspect of the experimental situation that is manipulated by the researcher - or changes naturally - so the effect on the DV can be measured.
• Dependent variable (DV)


  The variable that is measured by the researcher. Any effect on the DV should be caused by the change in the IV.
• Operationalisation

  Clearly defining variables in terms of how they can be measured.
• Extraneous variable (EV)

  Any variable, other than the independent variable (IV), that may affect the dependent variable (DV) if it is not controlled. EVs are essentially nuisance variables that do not vary systematically with the IV.
• Confounding variables

  A kind of EV but the key feature is that a confounding variable varies systematically with the IV. Therefore we can't tell if any change in the DV is due to the IV or the confounding variable.
• Demand characteristics

  Any cue from the researcher or from the research situation that may be interpreted by participants as revealing the purpose of an investigation. This may lead to a participant changing their behaviour within the research situation.
• Investigator effects

  Any effect of the investigator's behaviour (conscious or unconscious) on the research outcome (the DV). This may include everything from the design of the study to the selection of, and interaction with, participants during the research process.
• Randomisation

  The use of chance methods to control for the effects of bias when designing materials and deciding the order of experimental conditions.
• Standardisation

  Using exactly the same formalised procedures and instructions for all participants in a research study.
• Experimental design

  The different ways in which participants can be organised in relation to the experimental conditions.
• Independent group design

  Participants are allocated to different groups where each group represents one experimental condition.
• Repeated measures

  All participants take part in all conditions of the experiment.
• Matched pairs design

  Pairs of participants are first matched on some variable(s) that may affect the dependent variable. Then one member of the pair is assigned to Condition A and the other to Condition B.
• Random allocation

  An attempt to control for participant variables in an independent groups design which ensures that each participant has the same chance of being in one condition as any other.
• Counterbalancing

  An attempt to control for the effects of order in a repeated measures design: half the participants experience the conditions in one order, and the other half in the opposite order.
• Strengths of independent groups

  Order effects are not a problem whereas they are a problem for repeated measures designs. Participants also are less likely to guess the aims.
• Limitation of independent groups

  The participants who occupy the different groups are not the same in terms of participant variables. If a researcher finds a mean difference between the groups on the dependent variable (DV) this may be more to do with participant variables than the effects of the IV. These differences may act as a confounding variable, reducing the validity of the findings. To deal with this problem researchers use random allocation.
• Limitation of independent groups

  They are less economical than repeated measures as each participant contributes a single result only. Twice as many participants would be needed to produce equivalent data to that collected in a repeated measures design. This increases the time/money spent on recruiting participants.
• Strengths of repeated measures

  Participant variables are controlled (therefore higher validity) and fewer participants are needed (therefore less time spent recruiting them).
• Limitation of repeated measures

  Each participant has to do at least two tasks and the order of these tasks may be significant (i.e. there are order effects). In the energy drink example, having the energy drink first may have a continuing effect when a participant drinks water afterwards. To deal with this, researchers use counterbalancing.
• Limitation of repeated measures

  Order effects arise because repeating two tasks could create boredom or fatigue that might cause deterioration in performance on the second task, so it matters what order the tasks are in. Alternatively, participants' performance may improve through the effects of practice, especially on a skill-based task - in this case participants would perform better on the second task. Order acts as a confounding variable.
• Strength of matched pairs

  Participants only take part in a single condition so order effects and demand characteristics are less of a problem.
• Limitations of matched pairs

  • Although there is some attempt to reduce participant variables in this design, participants can never be matched exactly. Even when identical twins are used as matched pairs, there will still be important differences between them that may affect the DV.
  • Matching may be time-consuming and expensive, particularly if a pre-test is required, so this is less economical than other designs.
• Laboratory (lab) experiment

  An experiment that takes place in a controlled environment within which the researcher manipulates the IV and records the effect on the DV, whilst maintaining strict control of extraneous variables.
• Field experiment

  An experiment that takes place in a natural setting within which the researcher manipulates the IV and records the effect on the DV.
• Natural experiment

  An experiment where the change in the IV is not brought about by the researcher but would have happened even if the researcher had not been there. The researcher records the effect on a DV they have decided on.
• Quasi-experiment

  A study that is almost an experiment but lacks key ingredients. The IV has not been determined by anyone (the researcher or any other person) - the 'variables' simply exist, such as being old or young. Strictly speaking this is not an experiment.
• strength of laboratory experiments

  Lab experiments have high control over confounding (CVs) and extraneous variables (EVs). This means that the researcher can ensure that any effect on the dependent variable (DV) is likely to be the result of manipulation of the independent variable (IV). Thus, we can be more certain about demonstrating cause and effect (high internal validity).
• Strength of laboratory experiments

  Replication is more possible than in other types of experiment because of the high level of control. This ensures that new extraneous variables are not introduced when repeating an experiment. Replication is vital to check the results of any study to see whether the finding is valid and not just a one-off.
• Limitation of laboratory experiments

  Lab experiments may lack generalisability. The lab environment may be rather artificial and not like everyday life. In an unfamiliar context participants may behave in unusual ways so their behaviour cannot always be generalised beyond the research setting (low external validity).
• Limitation of laboratory experiments

  Participants are usually aware they are being tested in a lab experiment (though they may not know why) and this may also give rise to 'unnatural' behaviour (see demand characteristics described on page 170)
• Limitation of laboratory experiments

  the tasks participants are asked to carry out in a lab experiment may not represent everyday experience; for instance, recalling unconnected lists of words as part of a memory experiment (low mundane realism).
• Strength of field experiments

  Field experiments have higher mundane realism than lab experiments because the environment is more natural. Thus field experiments may produce behaviour that is more valid and authentic. This is especially the case as participants may be unaware they are being studied (high external validity).
• Limitations of field experiments

  • There is a price to pay for increased realism due to the loss of control of CVs and EVs. This means cause and effect between the IV and the DV in field studies may be much more difficult to establish and precise replication is often not possible.
  • There are also important ethical issues. If participants are unaware they are being studied they cannot consent to being studied and such research might constitute an invasion of privacy.